Triple

T29994810
Position Surface form Disambiguated ID Type / Status
Subject Northern Region, Malawi E761989 entity
Predicate hasMountainRange P651 FINISHED
Object Viphya Mountains
The Viphya Mountains are a highland range in northern Malawi known for their extensive pine plantations, cool climate, and scenic forested landscapes.
 E1898267 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Viphya Mountains | Statement: [Northern Region, Malawi, hasMountainRange, Viphya Mountains]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Viphya Mountains
Triple: [Northern Region, Malawi, hasMountainRange, Viphya Mountains]
Generated description
The Viphya Mountains are a highland range in northern Malawi known for their extensive pine plantations, cool climate, and scenic forested landscapes.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f224695498819094a81037cad401e2 completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f6791d8ecc8190a51ebe4ebfd25f6c completed May 2, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a274308a14081909c9221316bebece4 completed June 8, 2026, 10:32 p.m.
NEDg Description generation batch_6a2743e893708190a11e3888456906bb completed June 8, 2026, 10:36 p.m.
NED2 Entity disambiguation (via description) batch_6a274507fa3c819098819c5b1c2c4133 completed June 8, 2026, 10:41 p.m.
Created at: April 29, 2026, 6:39 p.m.